Dynamic virtual hearing modelling

ABSTRACT

Presented herein are techniques for generating, updating, and/or using a virtual hearing model associated with a recipient of an auditory prosthesis. The virtual hearing model is generated and updated for the recipient based on psychoacoustics data associated with the recipient and, in certain cases, based on psychoacoustics data gathered from one or more selected populations of auditory prosthesis recipients. The recipient-specific virtual hearing model can be used, in real-time, to determine one or more settings for the auditory prosthesis.

BACKGROUND Field of the Invention

The present invention generally relates to auditory prostheses.

Related Art

Hearing loss is a type of sensory impairment that is generally of twotypes, namely conductive and/or sensorineural. Conductive hearing lossoccurs when the normal mechanical pathways of the outer and/or middleear are impeded, for example, by damage to the ossicular chain or earcanal. Sensorineural hearing loss occurs when there is damage to theinner ear, or to the nerve pathways from the inner ear to the brain.

Individuals who suffer from conductive hearing loss typically have someform of residual hearing because the hair cells in the cochlea areundamaged. As such, individuals suffering from conductive hearing losstypically receive an auditory prosthesis that generates motion of thecochlea fluid. Such auditory prostheses include, for example, acoustichearing aids, bone conduction devices, and direct acoustic stimulators.

In many people who are profoundly deaf, however, the reason for theirdeafness is sensorineural hearing loss. Those suffering from some formsof sensorineural hearing loss are unable to derive suitable benefit fromauditory prostheses that generate mechanical motion of the cochleafluid. Such individuals can benefit from implantable auditory prosthesesthat stimulate nerve cells of the recipient's auditory system in otherways (e.g., electrical, optical and the like). Cochlear implants areoften proposed when the sensorineural hearing loss is due to the absenceor destruction of the cochlea hair cells, which transduce acousticsignals into nerve impulses. An auditory brainstem stimulator is anothertype of stimulating auditory prosthesis that might also be proposed whena recipient experiences sensorineural hearing loss due to damage to theauditory nerve.

SUMMARY

In one aspect, a method is provided. The method comprises: receiving,from an auditory prosthesis configured to be worn by a recipient, arequest for updated sound processing settings, wherein the requestincludes listening situation data representing an expected listeningsituation for the auditory prosthesis; determining, based on arecipient-specific virtual hearing model accounting for how the auditoryprosthesis operates and aids the perception of the recipient, selectedsound processing settings for use by the auditory prosthesis in theexpected listening situation; and sending the selected sound processingsettings to the auditory prosthesis.

In another aspect, a method is provided. The method comprises:generating, based on recipient-specific psychoacoustics data, a virtualhearing model, wherein the virtual hearing model is holistic model ofthe hearing system of a recipient of an auditory prosthesis, and whereinthe virtual hearing model accounts for, in a bilateral manner, operationof each of the outer ears, middle ears, and inner ear systems of therecipient, as well as the hearing cognition in auditory cortex and brainof the recipient, and how the auditory prosthesis operates and aids theperception of the recipient; using the virtual hearing model todetermine selected sound processing settings for use by the auditoryprosthesis; and instantiating the selected sound processing settings atthe auditory prosthesis.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are described herein in conjunctionwith the accompanying drawings, in which:

FIG. 1A is a schematic diagram illustrating a cochlear implant, inaccordance with certain embodiments presented herein;

FIG. 1B is a block diagram of the cochlear implant of FIG. 1A;

FIG. 2 is a schematic diagram illustrating operation of a portion of thetechniques presented herein to generate, update, and/use arecipient-specific virtual hearing model, in accordance with certainembodiments presented herein;

in FIG. 3 is a flowchart of a method, in accordance with certainembodiments presented herein;

FIG. 4 is a block diagram of a computing system configured to implementaspects of the presented techniques, in accordance with certainembodiments presented herein;

FIG. 5 is a functional block diagram illustrating a bone conductiondevice, in accordance with certain embodiments presented herein; and

FIG. 6 is a flowchart of a method, in accordance with certainembodiments presented herein.

DETAILED DESCRIPTION

Presented herein are techniques for generating, updating, and/or using avirtual hearing model associated with a recipient of an auditoryprosthesis. The virtual hearing model is generated and updated for therecipient based on psychoacoustics data associated with the recipientand, in certain cases, based on psychoacoustics data gathered from oneor more selected populations of auditory prosthesis recipients. Therecipient-specific virtual hearing model can be used, in real-time, todetermine one or more settings for the auditory prosthesis.

Merely for ease of description, the techniques presented herein areprimarily described herein with reference to one illustrativeauditory/hearing prosthesis, namely a cochlear implant. However, it isto be appreciated that the techniques presented herein may also be usedwith a variety of other types of devices, including other auditoryprostheses. For example, the techniques presented herein may beimplemented in, for example, acoustic hearing aids, auditory brainstemstimulators, bone conduction devices, middle ear auditory prostheses,direct acoustic stimulators, bimodal auditory prosthesis, bilateralauditory prosthesis, etc. As such, description of the invention withreference to a cochlear implant should not be interpreted as alimitation of the scope of the techniques presented herein.

FIG. 1A is ab schematic diagram of an exemplary cochlear implant 100configured to implement aspects of the techniques presented herein,while FIG. 1B is a block diagram of the cochlear implant 100. For easeof illustration, FIGS. 1A and 1B will be described together.

As shown, the recipient has an outer ear 101, a middle ear 103 and aninner ear 105. Elements of outer ear 101, middle ear 103 and inner ear105 are described below, followed by a description of cochlear implant100.

In a fully functional human hearing anatomy, outer ear 101 comprises anauricle 121 and an ear canal 109. A sound wave or acoustic pressure 107is collected by auricle 121 and channeled into and through ear canal109. Disposed across the distal end of ear canal 109 is a tympanicmembrane 111 which vibrates in response to acoustic wave 107. Thisvibration is coupled to oval window or fenestra ovalis 123 through threebones of middle ear 103, collectively referred to as the ossicles 113and comprising the malleus 115, the incus 117 and the stapes 119. Theossicles 113 of middle ear 103 serve to filter and amplify acoustic wave107, causing oval window 123 to vibrate. Such vibration sets up waves offluid motion within cochlea 133. Such fluid motion, in turn, activateshair cells (not shown) that line the inside of cochlea 133. Activationof the hair cells causes appropriate nerve impulses to be transferredthrough the spiral ganglion cells and auditory nerve 125 to the brain(not shown), where they are perceived as sound.

The cochlear implant 100 comprises an external component 102 and aninternal/implantable component 104. The external component 102 isdirectly or indirectly attached to the body of the recipient andtypically comprises an external coil 106 and, generally, a magnet (notshown in FIG. 1 ) fixed relative to the external coil 106. The externalcomponent 102 also comprises one or more input elements/devices 134 forreceiving input signals at a sound processing unit 112. In this example,the one or more input devices 134 include a plurality of microphones 108(e.g., microphones positioned by auricle 110 of the recipient,telecoils, etc.) configured to capture/receive input acoustic/soundsignals (sounds), one or more auxiliary input devices 129 (e.g., atelecoil, one or more audio ports, such as a Direct Audio Input (DAI), adata port, such as a Universal Serial Bus (USB) port, cable port, etc.),and a wireless transmitter/receiver (transceiver) 135, each located in,on, or near the sound processing unit 112.

The sound processing unit 112 also includes, for example, at least onebattery 127, a radio-frequency (RF) transceiver 131, and a processingblock 150. The processing block 150 comprises a number of elements,including a sound processing module 152 and a psychoacoustics monitoringsystem 154. As described further below, in certain embodiments thepsychoacoustics monitoring system 154 includes an environmentalclassifier 156. Each of the sound processing module 152 and thepsychoacoustics monitoring system 154 may be formed by one or moreprocessors (e.g., one or more Digital Signal Processors (DSPs), one ormore uC cores, etc.), firmware, software, etc. arranged to performoperations described herein. That is, the sound processing module 152and the psychoacoustics monitoring system 154 may each be implemented asfirmware elements, partially or fully implemented with digital logicgates in one or more application-specific integrated circuits (ASICs),partially or fully in software, etc.

As described further below, the psychoacoustics monitoring system 154 isconfigured to, in certain examples, monitor or analyze sound signalsreceived at the one or more input devices 134. More specifically, thepsychoacoustics monitoring system 154 may be configured to capture,extract, determine, or otherwise obtain recipient-specificpsychoacoustics data from the sound signals for use in generating orupdating a recipient-specific virtual hearing model for the recipient.Additionally or alternatively, also as described below, thepsychoacoustics monitoring system 154 may be configured to initiate arequest for updated settings, such as updated sound processing settings,for the cochlear implant 100. The request for updated settings mayinclude so-called listening situation data representing an expectedlistening situation for the recipient of cochlear implant 100. Thelistening situation data is generated, by the psychoacoustics monitoringsystem 154, from the sound signals received at the one or more inputdevices 134. Further details of the psychoacoustics monitoring system154, which is on-board (i.e., integrated in) cochlear implant 100, areprovided below.

As noted, in certain embodiments, the psychoacoustics monitoring system154 may include an environmental classification module (environmentalclassifier) 156 that is configured to evaluate/analyze the receivedsound signals (sounds) and determine the soundclass/category/environment of the sounds. That is, the environmentalclassifier 156 is configured to use the received sounds to “classify”the ambient sound environment and/or the sounds into one or more soundcategories (i.e., determine the input signal type). The sound class orenvironment may include, but are not limited to, “Speech” (e.g., thesound signals include primarily speech signals), “Noise” (e.g., thesound signals include primarily noise signals), “Speech+Noise” (e.g.,both speech and noise are present in the sound signals), “Wind” (e.g.,the sound signals include primarily wind signals), “Music” (e.g., thesound signals include primarily music signals), and “Quiet” (e.g., thesound signals include minimal speech or noise signals). Theenvironmental classifier 156 may also estimate the signal-to-noise ratio(SNR) of the sounds.

Returning to the example embodiment of FIGS. 1A and 1B, the implantablecomponent 104 comprises an implant body (main module) 114, a lead region116, and an intra-cochlear stimulating assembly 118, all configured tobe implanted under the skin/tissue (tissue) 145 of the recipient. Theimplant body 114 generally comprises a hermetically-sealed housing 115in which RF interface circuitry 124 and a stimulator unit 120 aredisposed. The implant body 114 also includes an internal/implantablecoil 122 that is generally external to the housing 115, but which isconnected to the RF interface circuitry 124 via a hermetic feedthrough(not shown in FIG. 1B).

As noted, stimulating assembly 118 is configured to be at leastpartially implanted in the recipient's cochlea 133. Stimulating assembly118 includes a plurality of longitudinally spaced intra-cochlearelectrical stimulating contacts (electrodes) 126 that collectively forma contact or electrode array 128 for delivery of electrical stimulation(current) to the recipient's cochlea. Stimulating assembly 118 extendsthrough an opening in the recipient's cochlea (e.g., cochleostomy, theround window, etc.) and has a proximal end connected to stimulator unit120 via lead region 116 and a hermetic feedthrough (not shown in FIG.1B). Lead region 116 includes a plurality of conductors (wires) thatelectrically couple the electrodes 126 to the stimulator unit 120.

As noted, the cochlear implant 100 includes the external coil 106 andthe implantable coil 122. The coils 106 and 122 are typically wireantenna coils each comprised of multiple turns of electrically insulatedsingle-strand or multi-strand platinum or gold wire. Generally, a magnetis fixed relative to each of the external coil 106 and the implantablecoil 122. The magnets fixed relative to the external coil 106 and theimplantable coil 122 facilitate the operational alignment of theexternal coil with the implantable coil. This operational alignment ofthe coils 106 and 122 enables the external component 102 to transmitdata, as well as possibly power, to the implantable component 104 via aclosely-coupled wireless link formed between the external coil 106 withthe implantable coil 122. In certain examples, the closely-coupledwireless link is a radio frequency (RF) link. However, various othertypes of energy transfer, such as infrared (IR), electromagnetic,capacitive and inductive transfer, may be used to transfer the powerand/or data from an external component to an implantable component and,as such, FIG. 1B illustrates only one example arrangement.

As noted above, the processing block 150 includes sound processingmodule 152. The sound processing module 152 (e.g., one or moreprocessing elements implementing firmware, software, etc.) is configuredto, in general, convert input sound signals into stimulation controlsignals 136 for use in stimulating a first ear of a recipient (i.e., thesound processing module 152 is configured to perform sound processing oninput sound signals received at the one or more input devices 134 togenerate signals 136 that represent electrical stimulation for deliveryto the recipient). The input sound signals that are processed andconverted into stimulation control signals may be audio signals receivedvia the microphones 108 or any of the other input devices 134.

In the embodiment of FIG. 1B, the stimulation control signals 136 areprovided to the RF transceiver 131, which transcutaneously transfers thestimulation control signals 136 (e.g., in an encoded manner) to theimplantable component 104 via external coil 106 and implantable coil122. That is, the stimulation control signals 136 are received at the RFinterface circuitry 124 via implantable coil 122 and provided to thestimulator unit 120. The stimulator unit 120 is configured to utilizethe stimulation control signals 136 to generate electrical stimulationsignals (e.g., current signals) for delivery to the recipient's cochleavia one or more stimulating contacts 126. In this way, cochlear implant100 electrically stimulates the recipient's auditory nerve cells,bypassing absent or defective hair cells that normally transduceacoustic vibrations into neural activity, in a manner that causes therecipient to perceive one or more components of the input audio signals.

FIGS. 1A and 1B illustrate an arrangement in which the cochlear implant100 includes an external component. However, it is to be appreciatedthat embodiments of the present invention may be implemented in cochlearimplants having alternative arrangements. For example, elements of thesound processing unit 112 (e.g., such as the processing block 150, powersource 102, etc.), may be implanted in the recipient.

It is also to be appreciated that the individual components referencedherein, e.g., microphones 108, auxiliary inputs 129, processing block150, etc., may be distributed across more than one prosthesis, e.g., twocochlear implants 100, and indeed across more than one type of device,e.g., cochlear implant 100 and a consumer electronic device or a remotecontrol of the cochlear implant 100.

As noted, the sound processing module 152 is configured to convert inputsound signals into stimulation control signals 136 for use instimulating a first ear of a recipient. The sound processing module 152processes the sound signals in accordance with various operatingparameters dictated by one of a number of selectable settings or modesof operation. The various selectable settings or modes of operation maybe in the form of executable programs or sets of parameters for use in aprogram. The settings may accommodate any of a number of specificconfigurations that influence the operation of the cochlear implant. Forexample, the settings may include different digital signal and soundprocessing algorithms, processes and/or operational parameters fordifferent algorithms, other types of executable programs (such as systemconfiguration, user interface, etc.), or operational parameters for suchprograms. In certain examples, the selectable settings would be storedin a memory of the cochlear implant 100 and relate to different optimalsettings for different listening situations or environments encounteredby the recipient (i.e., noisy or quite environments, windy environments,etc.).

Additionally, since the dynamic range for electrical stimulation isrelatively narrow and varies across recipients and stimulating contacts,programs used in sound processing module 152 may be individuallytailored to optimize the perceptions presented to the particularrecipient (i.e., tailor the characteristics of electrical stimulationfor the recipient). For example, many speech processing strategies relyon a customized set of stimulation settings which provide, for aparticular recipient, the threshold levels (T-levels) and comfortablelevels (C-levels) of stimulation for each frequency band. Once thesestimulation settings are established, the sound processor may thenoptimally process and convert the received acoustic signals intostimulation data for use by the stimulator unit 120 in deliveringstimulation signals to the recipient.

As such, it is clear that a typical cochlear implant has many parameterswhich determine the sound processing operations of the device. Theindividualized programs, commands, data, settings, parameters,instructions, modes, and/or other information that define the specificcharacteristics used by cochlear implant 100 to process electrical inputsignals and generate stimulation data therefrom are generally andcollectively referred to as the recipient's “MAP” or, more generally,the cochlear implant or auditory prosthesis “sound processing settings.”As described further below, presented herein are techniques forselecting and/or dynamically adjusting, potentially in real-time, thesound processing settings of an auditory prosthesis, such as cochlearimplant 100, based on a recipient-specific (individualized) “virtualhearing model.”

As used herein, a recipient-specific or individualized “virtual hearingmodel” is an advanced and customized computing model representing thecomplete or holistic operation of the hearing system of the recipient ofan auditory prosthesis. That is, the recipient-specific virtual hearingmodel is able to receive, as inputs, representations of sounds and thenestimate how the recipient would “perceive” those represented sounds(i.e., what the recipient would likely hear if those sounds were inputto the recipient's actual ear). As described further below, therecipient-specific virtual hearing model can be used in advancedcomputer simulations to determine, potentially in real-time, optimalsound processing settings (e.g., algorithms, programs, etc.) for anauditory prosthesis, thereby improving the recipient's hearingperception. The recipient-specific virtual hearing model can be used into plan customized rehabilitation exercises for the recipient.

In accordance with embodiments presented herein, a recipient-specificvirtual hearing model is a holistic model of the hearing system of therecipient, meaning that the model covers (accounts for) each of theouter ear, middle ear, and inner ear systems, as well as some element ofthe hearing cognition in auditory cortex and brain, in a bilateralmanner (i.e., accounts for both ears of the recipient). Therecipient-specific virtual hearing model also accounts for how theauditory prosthesis (e.g., cochlear implant 100) operates and aids theperception of the recipient. As described further below, arecipient-specific virtual hearing model is generated based onrecipient-specific data/information, sometimes referred to herein asrecipient-specific psychoacoustics data, and, potentially, data gatheredfrom one or more selected populations of auditory prosthesis recipients.

FIG. 2 is a schematic diagram illustrating aspects of the techniquespresented herein to generating and/or updating, as well as using, arecipient-specific virtual hearing model in accordance with certainembodiments presented herein. More specifically, shown in FIG. 2 arefour (4) aspects of the techniques described herein, namely: datacollection 260, a recipient-specific virtual hearing model 262, asimulated virtual listening situation 264, and a simulated hearingprofile 266. For ease of description, each of these aspects aredescribed further below with reference to cochlear implant 100 of FIGS.1A and 1B.

Referring first to the data collection 260, this generally refers to aphase or portion of the techniques in which recipient-specific(individualized) data is gathered for subsequent use in generating orupdating the recipient-specific virtual hearing model 262. Morespecifically, most auditory prosthesis recipients begin their hearingrehabilitation journey with some form of medical evaluation anddiagnosis by an audiologist, clinician, doctor, or other medicalprofessional (collectively and generally referred to as “clinicians”herein). For individuals who are diagnosed with mild to severe hearingloss, they may be implanted with a cochlear implant or other auditory.Individuals with less sever hearing loss may instead receive hearingaids.

During the preliminary testing phase, as well as the pre-surgery andpost-surgery phases (if needed), a number of evaluations and/ormeasurements are performed on the recipient. These evaluations and/ormeasurements may take a number of different forms, such as regularhealth check-ups, fitting sessions, image scans, hearing performancetests, etc. These different evaluations and/or measurements eachgenerate a variety of recipient-specific “psychoacoustics data.” As suchherein, psychoacoustics data is data relating to, or characterizing, howthe specific recipient (potentially with an auditory prosthesis)perceives sound signals (sounds). Each piece of psychoacoustics datarelates to the operation/function, structure, shape, etc. of one or moreof the recipient's outer, middle, and/or inner ear, and, as such, mayinclude physical characteristics of the recipient's ear, physiologicalmeasures, nerve responses, hearing perception scores (e.g., speech innoise perception), etc. If the recipient is implanted with, or uses, anauditory prosthesis, the psychoacoustics data may characterize how therecipient perceives sounds with the aid of the auditory prosthesis. Therecipient-specific psychoacoustics data could include, for example,physical measurements (e.g., optical measurements) of the recipient'sear(s) or other physical characteristics, histograms, audiograms,performance tests, age, disease states, etc.

In accordance with embodiments presented herein, the recipient-specificpsychoacoustics data, represented in FIG. 2 by arrows 258, may becollected from a number of different sources in a number of differentmanners and locations. For example, as described elsewhere herein, thepsychoacoustics data 258 may be gathered by the psychoacousticsmonitoring system 154 in the cochlear implant 100. Alternatively, thepsychoacoustics data 258 may be gathered via fitting systems or othercomputing devices in a clinic, hospital, the recipient's home, etc. FIG.2 illustrates one example computing device 261 (e.g., computer) that isconfigured to gather/capture/obtain psychoacoustics data 258 inaccordance with certain embodiments presented herein. As noted, it is tobe appreciated that the computing device 261 is merely illustrative andthat, in practice, a recipient's psychoacoustics data 258 could begathered from a large number of different devices, locations, etc.

In accordance with embodiments presented herein, the recipient-specificpsychoacoustics data 258 (e.g., captured by the computing device 261,cochlear implant 100, etc.) are collected and stored at a computingsystem 263 comprised of one or more computing devices 265. The one ormore computing devices 265 may, as represented by arrows 266 in FIG. 2 ,be in wireless communication with the computing device 261, cochlearimplant 100, or any other psychoacoustics data collection devices.

In certain embodiments, the computing system 263 may be a remote (e.g.,cloud-based) system and the computing devices 265 may be, for example,servers. In other embodiments, the computing system 263 may be a localsystem and the computing devices 265 may be, for example, servers,computers, mobile computing devices, etc. In still other embodiments,the computing system 263 may include both local computing devices (e.g.,a mobile computing device carried by a recipient) and remote devices(e.g., servers at a central processing location).

In certain embodiments, the cochlear implant 100 may have the capabilityto communicate directly with the computing system 263 (e.g., via acommunications network, via a short-range wireless connection, etc.),where the computing system 263 may be a local device (e.g., mobilephone) or a remote (e.g., cloud-based) system. In such embodiments, therequest for updated sound processing settings is received directly fromthe cochlear implant 100. However, in other embodiments, the cochlearimplant 100 may rely on a local device, such as a mobile phone, tocommunicate with the computing system 263.

As noted, the computing system 263 (e.g., computing devices 265) isconfigured to store the recipient-specific psychoacoustics data 258. Thecomputing system 263 is further configured to use the recipient-specificpsychoacoustics data 258 to generate/construct the recipient-specificvirtual hearing model 262. As noted above, the recipient-specificvirtual hearing model 262 is an advanced and customized computing modelgenerated from the recipient-specific psychoacoustics data 258 so as torepresent how the recipient's hearing system operates, in a bilateralmanner, to convert sound signals into sound perceptions. That is, therecipient-specific virtual hearing model 262 is able to receive, asinputs, representations of sounds and then estimate how the recipient islikely to perceive those represented sounds (i.e., what the recipientwould likely hear if those sounds were input to the recipient's actualhearing system).

The recipient-specific virtual hearing model 262 is generated andexecuted at the computing system 263 by a machine-trained expert system268 (i.e., the recipient-specific virtual hearing model 262 is generatedand/or updated using an expert system implementing machine-learningalgorithms and/or artificial intelligence algorithms). In certainembodiments, artificial intelligence (AI) may also be used in thegeneration or updating of the recipient-specific virtual hearing model262. Since the computing system 263 is separate from the cochlearimplant 100, there is potentially a large amount of processingcapabilities available to execute the model based on large parametersets. Accordingly, the computing system 263 has sufficient processingpower to use the machine-trained expert system 268 andrecipient-specific virtual hearing model 262 to perform advancedmodelling and computer simulations in terms of how a recipient is likelyto perceive various sounds.

With all the collected recipient-specific psychoacoustics data 258(e.g., physical characteristics, physiological measures, nerveresponses, etc.), the recipient-specific virtual hearing model 262 maybe, in form, a holistic model of the hearing system of the recipient,covering all of the outer ear, middle ear, and inner ear systems, aswell as some element of the hearing cognition in auditory cortex andbrain. In certain embodiments, in addition to the recipient-specificpsychoacoustics data 258, the recipient-specific virtual hearing model262 may be generated, at least in part, based on data gathered fromother recipients. For example, the recipient-specific virtual hearingmodel 262 could be generated or updated based on data obtained fromrecipients with similar types of hearing loss, with the same diseasesand/or pathologies, with similar hearing profiles, with similardemographics, etc.

In accordance with embodiments presented herein, the recipient-specificpsychoacoustics data 258 is collected when the recipient begins his/herrehabilitation journey and undergoes the implant surgery (i.e., generatethe hearing model based on the recipient's pre-surgery and post-surgerydata). In cochlear implants, a recipient generally undergoes a firstfitting or “switch-on” session some period of time after the surgery.This is the first point in time in which the cochlear implant isactivated and the recipient is able to use the implant to perceivesounds. Upon the first fitting/switch-on session, the recipient'shearing profile, which is a type of the recipient-specificpsychoacoustics data 258, would further be uploaded to the computingsystem 263. At this point, the machine-trained expert system 268generates the recipient-specific virtual hearing model 262.

The recipient-specific virtual hearing model 262 generally covers therecipient's entire hearing system, including both the left and rightears. This bilateral formation allows the impacts of various settings onthe ability of the recipient to localize sound to be determined. Therecipient-specific virtual hearing models present herein may also covercases where: (1) the recipient is a unilateral recipient (e.g., hearingaid, bone conduction, cochlear implant, or other auditory prosthesis atone ear, and normal hearing in the other ear); (2) the recipient is abimodal recipient (e.g., different types of hearing prostheses at eachear, such as a cochlear implant at one ear and a hearing aid at theother ear; or (3) the recipient is a bilateral recipient (e.g., the sametype of auditory prosthesis at both ears, such as two cochlearimplants), as needed.

As noted, the recipient-specific virtual hearing model 262 is builtusing recipient-specific psychoacoustics data 258, which includes datarelating to how the cochlear implant 100 aids the recipient's perception(i.e., the estimated perception is the perception of the recipient usingthe cochlear implant). As such, in accordance with embodiments presentedherein, the recipient-specific virtual hearing model 262 is can be usedin computer simulations to select, determine, and or dynamically adjustthe sound processing settings used by the cochlear implant 100 in amanner that improves the recipient's hearing perception performance.

More specifically, in accordance with embodiments presented herein, therecipient-specific virtual hearing model 262 can be used to simulateoperation of the recipient's hearing system in a number of differentsimulated/virtual “listening situations” (i.e., the virtual hearingmodel is tested/simulated in a virtual listening situation). As usedherein, a virtual “listening situation” is a specific set of audiocircumstances that may be experienced by a recipient of an auditoryprosthesis (e.g., recipient of cochlear implant 100). The set of audiocircumstances forming a virtual listening situation extend beyond asimple environmental classification (e.g., “Speech,” “Noise,” etc.).That is, in accordance with embodiments presented herein, thesimulations are performed with much more granularity and more detailthan simply different “sound environments” (e.g., Noise, Speech, etc.).

For example, a virtual listening situation in accordance withembodiments presented herein may take in account recipient-specificfactors, such as the person's cognitive load, age/aging, heredityattributes (e.g., related to hearing), emotional state, etc. In certainembodiments, the virtual listening situation can account for externalfactors, such non-verbal cues such as the tone of the voice (if there isspeech), gestures, body language, etc. In certain embodiments, thevirtual listening situation may include a determination of the “type” ofperson producing the speech. For example, a person may listen moreintensively if he/she is interacting with a work supervisor, compared towhen he/she is interacting with peers. In another example, a person maylisten intensively when he/she is engaged in a telephone conversationsince the other cues (e.g., gestures, body language, etc.) related tothe message/information are not available. Oher listening situationscould vary greatly and may depend on how the generated information(simulation results) will be used (e.g., listening to something just forrelaxation compared to listening to something in order to learn).

Shown in FIG. 2 is an example simulated virtual listening situation 264.In accordance with the techniques presented herein, the computing system263 includes a profile simulator 278 that simulates operation of therecipient-specific virtual hearing model 262 in a number of differentvirtual listening situations a number of different times. Throughtesting the recipient-specific virtual hearing model 262 in one or moresimulated virtual listening situations, the machine-trained expertsystem 268 is able to learn the settings and/or adjustment for cochlearimplant 100 that would introduce the best optimal hearing perception inthe recipient-specific virtual hearing model 262 and, accordingly, forthe actual recipient.

That is, as noted above, the recipient-specific virtual hearing model262 inherently incorporates the operation of the cochlear implant 100(and/or another auditory prosthesis, if present). Therefore, with eachsimulation iteration, the recipient-specific virtual hearing model 262is also simulating how the recipient's cochlear implant 100 would likelyperform and affect the recipient's hearing perception, in the event therecipient were to encounter a real listening situation matching thevirtual listening situation. During these simulations, the performanceof cochlear implant 100 is simulated using virtual/simulated soundprocessing settings.

In particular, in accordance with embodiments presented herein, themachine-trained expert system 268 includes a machine-learning algorithmthat is trained to learn what settings and/or adjustment to operation ofcochlear implant 100 would bring out the optimal hearing perception onthe recipient-specific virtual hearing model 262 in a given listeningsituation. The machine-learning algorithm of the machine-trained expertsystem 268 is configured to simulate operation of the recipient-specificvirtual hearing model 262 in a number of different listening situations(e.g., shopping center, restaurant, concert hall, classroom, office,special sets of spatial sound sources, etc.) with different soundprocessing settings in each of the different listening situationswithout interrupting the recipient's hearing or routine. Therefore, invarious simulation iterations of the recipient-specific virtual hearingmodel 262, the simulated sound processing settings for the cochlearimplant 100 can be changed/adjusted to determine whether the cochlearimplant 100, and thus the recipient-specific virtual hearing model 262in general, would provide better or worse sound perception for therecipient in the associated virtual listening situation. As describedfurther below, the simulated scenarios can be, in certain examples,based on the real-time environments that the recipient is experiencing,or scenarios that the recipient has not yet experienced, but arepredicted to be experienced by the recipient.

Over time, the simulation of the recipient-specific virtual hearingmodel 262 in different virtual listening situations generates a“simulated hearing profile” 266 for the recipient. As used herein, thesimulated hearing profile 266 is a collection of simulated outputs fromexecution of the recipient-specific virtual hearing model 262. Therecipient's simulated hearing profile 266 can be used to generate thesettings (e.g., MAP) that can be instantiated (e.g., sent to, installed,and activated) at a recipient's auditory prosthesis.

For example, assume a scenario in which a system (e.g., smart homesystem that the recipient is using at home) detects (via sensors) thatthe recipient enters the laundry room to operate the washing machine.The system would initiate a connection to the cloud to obtain asimulated profile (e.g., a profile of operation of the recipient'sprosthesis when operating the washing machine in the laundry room) insuch a way that the settings determined to be optimal (e.g., threshold(T) levels, comfort (C) levels, etc.) are sent to the prosthesis andused to update the T and C levels at certain frequency band(s). Inanother example scenario, a recipient enters a library (e.g., quietenvironment) and sits at a table to read, where the place he/she sits iscloser to a wall or near the corner of the room (e.g., reverberation orrepercussion). Based on the geographical floor location, a system (e.g.,recipient's smart phone or GPS sensor) sends information to the cloud.The cloud then searches for a simulated profile where simulations havebeen performed in a “Quiet” environment (since the library is likely a“Quiet” sound environment) and in combination with repercussionsuppression information (due to the nearness of the wall(s).

In certain embodiments, the machine-trained expert system 268 includes arating sub-system which enables the machine-trained expert system 268 tolearn what benchmark(s) should be achieved when the system is creatingand/or running different simulated listening situations with therecipient-specific virtual hearing model 262. These benchmarks could bebased on, for example, data obtained from a larger population ofrecipients. For example, the data generated from simulating virtualhearing models associated with many other recipients can be compared andcorrelated, with the impacts of various sound processing settings onother similar recipients in the population being analyzed to betterunderstand how changes will best be made to the auditory prosthesis usedby a given recipient.

In general, the recipient-specific virtual hearing model 262 can beupdated over time based on the results of the simulations. Additionally,the recipient-specific virtual hearing model 262 can be updated, againover time, as new psychoacoustic data is received throughout thelifetime of the recipient from, for example, psychoacoustics monitoringsystem 154, computing system 261, and/or other sources. This newrecipient-specific psychoacoustics data 258 may be, for example, fromfitting session data, data from the post-surgery MRI or other imagingscans, data from the regular health check-ups, physiological measures atthe beginning of each week/month, background objective measures takenfrom the running devices, device setting changes by the recipient,device usage logs, etc. If new psychoacoustic data is received, therecipient-specific virtual hearing model 262 can be (e.g., periodically)re-simulated in different listening situations and the simulated hearingprofile 266 can accordingly be updated.

As noted above, the computing system 263 and, more specifically themachine-trained expert system 268, operates in conjunction with thepsychoacoustics monitoring system 154 in the cochlear implant 100. Thepsychoacoustics monitoring system 154 is generally configured to monitor(e.g., continuously) the recipient's real-time ambient environment andprovide indications of a recipient's “expected listening situation” tothe machine-trained expert system 268. As used herein, a recipient's“expected listening situation” is a specific set of audio circumstancesthat recipient has encountered, or is likely to encounter in the nearfuture. As noted above, similar to the virtual listening situation, theset of audio circumstances forming an expected listening situationextend beyond a simple environmental classification (e.g., “Speech,”“Noise,” etc.) and also include details such as recipient-specificfactors, external factors, etc.

In accordance with certain embodiments presented herein, the recipient'sexpected listening situation is determined from the sound signalsreceived at the one or more input devices 134 of cochlear implant 100.In particular, the cochlear implant 100 (e.g., psychoacousticsmonitoring system 154) is configured to provide the computing system 263and, more specifically the machine-trained expert system 268, with“listening situation data,” which represents an expected listeningsituation for the auditory prosthesis. The listening situation data mayinclude, for example, a classification of the sound environment (e.g.,“Noise,” “Speech,” etc.), SNR, average speech and average noise levels,the rough spatial distribution of speech and noise sources, the dynamicrange and spectral characteristics of the various components of theauditory scene, other characteristics of the surrounding noiseenvironment (e.g., background music distribution and level), etc.

In certain embodiments, when the characteristics of the recipient'sexpected listening situation are found to be closely matched to asimulated listening situation, the machine-trained expert system 268 canselect the associated sound processing settings (e.g., processingalgorithm, T and C levels, noise masker, activation/deactivation ofparticular operational features, the gain setting on a particularelectrode or groups of electrodes, the delay timing alignment betweenthe acoustic and electrical sound paths, the chip processor powercontrol/consumption/speed, etc.) determined to be most optimal in thatmatched simulated listening situation and push these onto therecipient's cochlear implant 100 for instantiation and use. In certainexamples, the machine-trained expert system may even suggest changesoutside of the prosthesis itself. For instance, for a recipient ho hastinnitus, besides applying the noise masker, the expert system mayadvise the recipient to turn on a device/speaker to broadcast or streamover the soft natural sound to soothe the tinnitus.

For example, in accordance with embodiments presented herein, thepsychoacoustics monitoring system 154 in cochlear implant 100 can beconfigured to determine that the recipient is experiencing, or is likelyto experience, a certain listening situation. The psychoacousticsmonitoring system 154 can provide data representing an expectedlistening situation to the machine-trained expert system 268, which inturn can select a set of sound processing settings (determined based onthe recipient-specific virtual hearing model 262 analysis in differentsimulated environments) and provide these selected set of soundprocessing settings to the cochlear implant 100 for instantiation anduse (e.g., for use by sound processing module 152, etc.). For example,the machine-learning algorithm in the machine-trained expert system 268can determine the most likely matched virtual listening situation (basedon the data received from the cochlear implant 100). Using the matchedvirtual listening situation, the machine-learning algorithm can selectthe most optimal set of sound processing settings for the cochlearimplant 100 in the expected listening situation. Further examples ofsuch real-time determination of auditory prosthesis settings aredescribed in further detail below.

Since the recipient-specific virtual hearing model 262 represents therecipient's natural auditory system, from the diagnostic perspective,clinicians could use the model to predict and understand the physicalpotential capability and limitations of the recipient's hearingperception. Effectively, by looking at the recipient's virtual hearingmodel 262 that will be updated continuously as the recipient is gettingolder, potentially the shortcomings of the auditory system could benoticed well in advance. As such, diagnostic, rehabilitation, and otherexercises that could be planned accordingly. For instance, the effect ofa specific rehabilitation strategy, or sound processing settings change,could be simulated prior to being applied, and the likely effect thereofon the hearing performance can be predicted.

In certain embodiments, the proposed system could have the advantage ofkeeping, comparing and/or even deducing (with the usage of artificialintelligence) what the recipient's virtual hearing model 262 could belike in the future (e.g., in one year, in five years, in ten years,etc.). For someone who is prone to suffer from ear discomfort (e.g., earbarotrauma) due to pressure changes, the recipient-specific virtualhearing model 262 could factor in such an item and simulate theenvironmental changes in such a way to let the recipient and/or theclinician be aware of the situation so that customized precautions couldbe taken if the recipient is going to travel or move to a location atwhich he/she would experience such pressure changes. The modelling canbe based on similar trends observed by other recipients in thepopulation that have similar or analogous simulated hearing profiles.

Another application of the recipient-specific virtual hearing model 262could be extended and applied onto the rehabilitation area. Most of theexisting rehabilitation programs require extensive time from both therecipient and his/her clinician to go through several trail-and-errorsessions in order to figure out the optimal rehabilitation for therecipient. The recipient may or may not be able to achieve the bestrehabilitation based on these subjective sessions. In accordance withembodiments presented herein, the recipient-specific virtual hearingmodel 262 could provide another tool that the clinician (or the wholeclinic) could use to identify an optimal individualized rehabilitationprogram/plan for the recipient (e.g., advanced modelling and computersimulations to select customized rehabilitation exercises for therecipient).

For example, the recipient-specific virtual hearing model 262 may beused to select customized rehabilitation exercises for the recipient. Incertain embodiments, with supplementary information (such as age,occupation, geographical location, life habits, etc.), a large number ofmodels with respect to different individuals in the population could beconstructed. These models could be further grouped into different usergroups. For instance, a user group that shows the normal hearing at acertain age range, a user group containing the characteristics oftinnitus, a user group showing the characteristics or impact upon takingcertain medication, and so on. With all these user groups, the model ofan individual could be used and compared to the characteristics of themodels of those user groups.

In such examples, the models may show that, because of the hearing loss,there is a likelihood for an individual to experience high levels ofanxiety resulting in loss of concentration, inability to rememberconversations, and even could not listen to another party. In this case,the system would select exercises (specially focused on sounds) tostimulate the person's cognitive function or choose the games so as tohelp the individual to train to remember more, concentrate better, thinksharper, etc.

As described above, in accordance with embodiments presented herein, arecipient-specific virtual hearing model can be used in real-time todynamically select/adjust (optimize) the settings of a recipient'sauditory prosthesis. FIG. 3 is a flowchart illustrating an examplemethod 370 for dynamic or real-time use of a recipient-specific virtualhearing model in accordance with embodiments presented herein. For easeof illustration, the method 370 FIG. 3 will be described with referenceto cochlear implant 100 of FIGS. 1A and 1B and the computing system 263of FIG. 2 . However, it is to be appreciated that the method of FIG. 3may be implemented with other auditory prostheses, computing systems,etc.

More specifically, method 370 begins at 372 where the computing system263 receives, from cochlear implant 100, a request for updated soundprocessing settings. The request received from cochlear implant 100includes the listening situation data representing expected listeningscenario for the cochlear implant. In accordance with embodimentspresented herein, the “expected” listing situation can be acurrent/present listening scenario of the cochlear implant 100, or anestimated/predicted future listing scenario.

In general, the expected listening situation for the cochlear implant100 is determined by the psychoacoustics monitoring system 154 based, atleast in part, on the sound signals received at the cochlear implant.More specifically, in one example, a system may be provided in which astructure (e.g., table) is defined to indicate the basic characteristicsor patterns of different listening situation. For instance, there couldbe a likelihood or reference score defined in the system. In practice,the information relating to such characteristics could be collected,monitored, and tracked in in real time. Through analyzing thecontinuously collected characteristics relating to the surroundings(related to the listening situation as well as the external one such asthe location of the recipient), the system will build a likelihoodreference and/or probability score. When that probability score (basedon all the relevant combined characteristics) is getting close to thereference score, the system would know that the recipient is situated inthe expected listening situation.

For example, in certain embodiments, the cochlear implant 100 isconfigured to sample and classify the sound environment (e.g., anenvironmental classifier the generates sound classificationinformation/data that is used to determine the expected listeningsituation), as well as to determine other sound parameters, such asaverage speech and average noise levels, the rough spatial distributionof speech and noise sources, the dynamic range and spectralcharacteristics of the various components of the auditory scene, othercharacteristics of the surrounding noise environment (e.g., backgroundmusic distribution and level), etc.

In certain embodiments, the cochlear implant 100 generates the requestfor the updated sound processing settings automatically (i.e., withoutinvolvement by the recipient). The automated request for the updatedsound processing settings could be sent by the cochlear implant 100 inresponse to detection of a number of different conditions (e.g.,detection of new sound environment or specific sound parameters, atcertain points in time, etc.). In other embodiments, the cochlearimplant 100 generates the request for the updated sound processingsettings in response to one or more user inputs received at the cochlearimplant 100 or another device (e.g., mobile phone, remote control, orother mobile computing device in communication with the cochlear implant100).

Returning to the example of FIG. 2 , in response to the request fromcochlear implant 100, at 374 the computing system 263 determines, basedon the virtual hearing model 263 representing operation of therecipient's hearing system, selected sound processing settings for useby the cochlear implant 100 in the expected listening situation. At 376,the selected sound processing settings are sent to the cochlear implant100 for instantiation and subsequent use by the cochlear implant 100.That is, once the selected sound processing settings are received at thecochlear implant 100, the cochlear implant 100 installs the selectedsound processing settings and begins processing sound signals usingthose settings.

The computing system 263 can use the virtual hearing model 262 todetermine the selected sound processing settings in a number ofdifferent manners. For example, as noted above, the computing system 263is configured to simulate the virtual hearing model 262 in differentvirtual listening situations (i.e., to predict how the recipient'shearing system, as aided by cochlear implant 100, would perceive soundsin different scenarios having different acoustical properties, differentnoise levels, etc.). In certain embodiments, these simulations can bepre-run so as cover a variety of different listening situations. Inthese embodiments, when the request for updated sound processing settingis received from the cochlear implant 100, the computing system 263,more specifically machine-trained expert system 268, determines which ofthe pre-performed simulated virtual listening situations most closelymatches the expected listening situations. The machine-trained expertsystem 268 that selects, as the selected sound processing settings, thesimulated sound processing settings that performed best in the mostclosely matching simulated virtual listening situation. That is, themost optimal sound processing settings for each simulated listeningsituation are stored in the computing system 263. Upon receiving therequest for updated sound processing from the cochlear implant 100, themachine-trained expert system 268 matches the expected listeningsituation to a simulated listening situation and then selects, as theselected sound processing parameters, the sound processing settingsdetermined to be most optimal (for the recipient-specific virtualhearing model 262 and thus for the recipient) in the associatedsimulated listening situation.

The above illustrates an example in which the data indicating theexpected listening situation is used by the machine-trained expertsystem 268 to identify a similar/analogous simulated listening situationand the sound processing parameters associated therewith. In otherembodiments, the data indicating the expected listening situation can beused, in real-time, to build or select a listening environment forsimulation and accordingly determine the selected sound processingparameters. That is, in these embodiments, the machine-trained expertsystem 268 uses the data included in the request from the cochlearimplant 100 to create, build, select, etc. a simulated listeningsituation that matches the expected listening situation of the recipientof cochlear implant 100. The machine-trained expert system 268 then runsreal-time simulations for the recipient-specific virtual hearing model262, and thus for the recipient, in the selected simulated listeningsituation to determine optimal sound processing settings for thecochlear implant 100 in the expected listening situation. As noted, uponcompletion of this analysis, the selected sound processing settings areprovided to the cochlear implant 100.

As noted above, the computing system 263 can use the recipient-specificvirtual hearing model 262 in a number of different manners to determinethe selected sound processing settings for cochlear implant 100. Assuch, it is to be appreciated that the above two techniques fordetermining the selected sound processing settings are merelyillustrative and that embodiments presented herein may use othertechniques for selection of the sound processing settings for cochlearimplant 100 or another auditory prosthesis.

To facilitate further understanding of the techniques presented,provided below are several examples of the real-time applicability of arecipient-specific virtual hearing model to selection of soundprocessing settings. For ease of illustration, these examples are againdescribed with reference to cochlear implant 100 of FIGS. 1A and 1B andthe computing system 263 of FIG. 2 . However, it is to be appreciatedthat similar techniques may be implemented with other auditoryprostheses, computing systems, etc.

In one example, the recipient of cochlear implant 100 enters a cocktailparty (i.e., an expected listening environment). The cochlear implant100 is configured to sample and classify the sound environment (e.g., anenvironmental classifier determines that the recipient is in a“Speech+Noise” environment), as well as determine other soundparameters, such as average speech and average noise levels, the roughspatial distribution of speech and noise sources, the dynamic range andspectral characteristics of the various components of the auditoryscene, other characteristics of the surrounding noise environment (e.g.,background music distribution and level), etc. This information,sometimes referred to herein as the listening situation data, representsthe expected listening situation (i.e., cocktail party) for the cochlearimplant 100. As noted, the listening situation data can be communicatedto the machine-trained expert system 268 in computing system 263. Themachine-trained expert system 268 is configured to then run simulationson the hearing perception of the recipient in the expected listeningsituation (i.e., cocktail party) and determine the optimal soundprocessing settings for this specific listening situation. Thesesettings then be communicated immediately to the recipient, inreal-time, to adjust the cochlear implant 100 in an optimal manner.

In accordance with embodiments herein, once an expected listeningsituation has been encountered by the recipient, the associated soundprocessing settings selected for use therein by machine-trained expertsystem 268 can be added to a database associated with the recipient. Assuch, these settings can be re-applied the next time the recipient is inthis or a similar listening situation without performing simulationsand/or only performing a validation process. Further, this expectedlistening situation and associated sound processing settings could beadded to a large, global database, to help fine-tune the fitting processand/or be used by other recipients when they are in similarenvironments.

In another illustrative example, the recipient of cochlear implant 100enters a concert hall to listen to a performance. A concert hall is aparticularly unusual listening situation for a recipient in that it withlikely include significant reverberation, important harmonics, andvarious directional sources of information and noise. In accordance withembodiments presented herein, the psychoacoustics monitoring system 154in the cochlear implant 100 is configured to sample the auditoryenvironment to gather the listening situation data. In certain examples,the listening situation data can be supplemented with auxiliaryenvironmental data from alternate sources, such as a database of soundlocations, information on the performance and/or the specific venue,occupancy information, etc. obtained at computing system 263 from one ormore auxiliary sources (e.g., other computing systems). Again, using thelistening situation data and, in certain examples the auxiliaryenvironmental data, the machine-trained expert system 268 is configuredto run simulations on the hearing perception of the recipient in theexpected listening situation (i.e., concert hall) and determine theoptimal sound processing settings for this specific listening situation.These settings then be communicated immediately to the recipient, inreal-time, to adjust the cochlear implant 100 in order to improve theperception and enjoyment of the concert for the recipient.

In another example, a recipient may take a bush or nature walk where theauditory environment experienced by the recipient may be quite differentfrom what the recipient experiences in everyday life (e.g., far lessauditory reflections and a background ‘noise’ that really isn't noiseand forms somewhat part of the experience of being in the bush). Inaccordance with embodiments presented herein, the psychoacousticsmonitoring system 154 in the cochlear implant 100 is configured tosample the auditory environment to gather the listening situation dataindicating the recipient is in this specific listening situation. Again,using the listening situation data, the machine-trained expert system268 is configured to run simulations on the hearing perception of therecipient in the expected listening situation (i.e., nature walk) anddetermine the optimal sound processing settings for this specificlistening situation. These settings then be communicated immediately tothe recipient, in real-time, to adjust the cochlear implant 100 in orderto improve the perception and enjoyment of the recipient while on thenature walk.

The above specific examples are merely illustrative to show that thetechniques presented herein could be used in any of a number ofdifferent circumstances to determine updated sound processing settingsfor cochlear implant 100 or another auditory prosthesis. The aboveexamples also illustrate that the techniques presented herein are ableto function somewhat like an advanced classifier, where the soundprocessing can be adapted based on a specific listening situation, withfar greater adaptability and granularity than simply using an on-boardenvironmental classifier. As such, building on the previous examples, incertain embodiments, the machine-trained expert system 268 can use therecipient-specific virtual hearing model 262 to continually runsimulations based on the listening situations experienced by therecipient. The machine-trained expert system 268 could then adjust thepsychoacoustics monitoring system 154 and/or the environmentalclassifier in the cochlear implant 100 for the specific recipient andpush this on to the recipient's sound processor. This can happenregularly or even continuously in real-time, based on the listeningsituations experienced by the recipient.

In further embodiments, the psychoacoustics monitoring system 154 in thecochlear implant 100 could itself include a machine-learning systemexecuting a recipient-specific virtual hearing model (e.g., implementedon an embedded AI chip). For example, the remote machine-trained expertsystem 268 could push/install a copy of the recipient-specific virtualhearing model 262 at the cochlear implant 100. The cochlear 100 may bethe complete virtual hearing model 262 or a modified version of thevirtual hearing model optimized to run in a lower-power environment. Inthese embodiments, the copy of the recipient-specific virtual hearingmodel 262 could be used, in a similar manner as described above withreference to the recipient-specific virtual hearing model 262 running incomputing system 263, to select sound processing settings for thecochlear implant 100 (e.g., run simulations in virtual listeningsituation to, via a machine-learning algorithm, determine optimalsettings for an expected listening situation). Over time, themachine-trained expert system 268 could push changes made to therecipient-specific virtual hearing model 262 to the psychoacousticsmonitoring system 154 to control and alter how the embeddedmachine-learning system learns and adapts. In this way, an embeddedmachine-learning system is trained and updated regularly by a morepowerful, cloud-based machine-learning system.

In other words, in these examples a low power machine-learning and/orartificial intelligence algorithm is embedded in cochlear implant 100(e.g., as part of psychoacoustics monitoring system 154). The morepowerful machine-trained expert system 268 in computing system 263(e.g., on a mobile phone, computer, server, etc.) is used to train theembedded system for the recipient based on their listening situationsand simulated hearing profile(s). At a regular basis, the embeddedsystem, including the copy of the recipient-specific virtual hearingmodel 262, can be updated by the machine-trained expert system 268,thereby providing improved performance without requiring the level ofprocessing on the cochlear implant 100 as required to train the morepowerful machine-trained expert system 268 in computing system 263.

As noted above, aspects of the techniques presented herein may beimplemented on a local or remote computing system comprising one or morecomputing devices. FIG. 4 is functional block diagram of an examplecomputing system 463 configured to implement aspects of the techniquespresented herein. In the example of FIG. 4 , the computing system 463includes a single computing device 465. It is to be appreciated thatthis implementation is merely illustrative and that computing system inaccordance with embodiments presented herein may be formed by one or aplurality of different computing devices. As such, it is to beappreciated that, in certain embodiments, the components shown in FIG. 4may be implemented across multiple computing devices.

In FIG. 4 , the computing device 465 comprises a plurality ofinterfaces/ports 489(1)-489(N), a memory 490, and one or more processors491. Although not shown in FIG. 4 , the computing device 465 could alsoinclude a user interface, display screen, etc., depending on the type ofcomputing device used to implement the techniques presented herein. Theinterfaces 489(1)-489(N) may comprise, for example, any combination ofnetwork ports (e.g., Ethernet ports), wireless network interfaces,Universal Serial Bus (USB) ports, Institute of Electrical andElectronics Engineers (IEEE) 1394 interfaces, PS/2 ports, etc.Interfaces 289(1)-289(N) may be configured to transmit/receive signalsvia a wired or wireless connection (e.g., telemetry, Bluetooth, etc.).

Memory 490 includes machine-learning logic 468, profile simulator logic478, and one or more simulated hearing profiles 466. Memory 490 maycomprise read only memory (ROM), random access memory (RAM), magneticdisk storage media devices, optical storage media devices, flash memorydevices, electrical, optical, or other physical/tangible memory storagedevices. The one or more processors 491 are, for example,microprocessors or microcontrollers that executes instructions for themachine-learning logic 468 and the profile simulator logic 478. Thus, ingeneral, the memory 490 may comprise one or more tangible(non-transitory) computer readable storage media (e.g., a memory device)encoded with software comprising computer executable instructions andwhen the software is executed (by the one or more processors 491) it isoperable to perform all or part of the presented techniques. Forexample, the machine-learning logic 468 may be executed by the one ormore processors 491 to, for example, perform the techniques describedabove with reference to machine-trained expert systems, such asmachine-trained expert system 268. The profile simulator logic 478 maybe executed by the one or more processors 491 to, for example, performthe techniques described above with reference to a profile simulator,such as profile simulator 278.

Merely for ease of description, the techniques presented herein haveprimarily been described herein with reference to one illustrativeauditory/hearing prosthesis, namely a cochlear implant. However, it isto be appreciated that the techniques presented herein may also be usedwith a variety of other types of devices, including other auditoryprostheses. For example, the techniques presented herein may beimplemented in, for example, acoustic hearing aids, auditory brainstemstimulators, bone conduction devices, middle ear auditory prostheses,direct acoustic stimulators, bimodal auditory prosthesis, bilateralauditory prosthesis, etc. FIG. 5 , in particular, is a functional blockdiagram of one example arrangement for a bone conduction device 500configured to implement embodiments presented herein.

Bone conduction device 500 comprises a microphone array 540, anelectronics module 512, a transducer 520, a user interface 524, and apower source 527.

The microphone array 540 comprises first and second microphones 508(1)and 508(2) configured to convert received sound signals (sounds) intomicrophone signals 544(1) and 544(2). The microphone signals 544(1) and544(2) are provided to electronics module 512. In general, electronicsmodule 512 is configured to convert the microphone signals 544(1) and544(2) into one or more transducer drive signals 518 that activatetransducer 520. More specifically, electronics module 512 includes,among other elements, at least one processor 550, a memory 532, andtransducer drive components 534.

The memory 532 includes sound processing logic 552 and psychoacousticsmonitoring logic 554. Memory 532 may comprise read only memory (ROM),random access memory (RAM), magnetic disk storage media devices, opticalstorage media devices, flash memory devices, electrical, optical, orother physical/tangible memory storage devices. The at least oneprocessor 550 is, for example, a microprocessor or microcontroller thatexecutes instructions for the sound processing logic 552 andpsychoacoustics monitoring logic 554. Thus, in general, the memory 532may comprise one or more tangible (non-transitory) computer readablestorage media (e.g., a memory device) encoded with software comprisingcomputer executable instructions and when the software is executed (atleast one processor 550) it is operable to perform aspects of thetechniques presented herein.

Transducer 520 illustrates an example of a stimulator unit that receivesthe transducer drive signal(s) 518 and generates stimulation(vibrations) for delivery to the skull of the recipient via atranscutaneous or percutaneous anchor system (not shown) that is coupledto bone conduction device 500. Delivery of the vibration causes motionof the cochlea fluid in the recipient's contralateral functional ear,thereby activating the hair cells in the functional ear.

Similar to cochlear implants, bone conduction devices and/or otherauditory prosthesis operate in accordance with a number of soundprocessing settings. As such, in accordance with embodiments of FIG. 5 ,a virtual hearing model may be generated for the recipient of boneconduction device 500 in a computing system (e.g., system 263, system463, etc.) based on recipient-specific psychoacoustics data capturedfrom the bone conduction device 500 (e.g., via execution ofpsychoacoustics monitoring logic 554 by at least one processor 550) orfrom another device.

Similar to the above embodiments, simulations may be performed usingthis virtual hearing model in a number of different virtual listeningsituations to generate a simulated hearing profile for the recipient ofbone conduction device 500. Subsequently, the psychoacoustics monitoringlogic 554 may, when executed by the at least one processor 550, generatea request to the computing system for a set of updated sound processingfor use by the bone conduction device 500 in an expected listeningsituation. The computing system may then determine, based on the virtualhearing model representing operation of the recipient's hearing system,selected sound processing settings for use by the bone conduction devicein the expected listening situation and send the selected soundprocessing settings to bone conduction device 500 for instantiation.

FIG. 6 is a flowchart of a method 692, in accordance with embodimentspresented herein. Method 692 begins at 693 where a virtual hearing modelis generated based on recipient-specific psychoacoustics data. Oncegenerated, the virtual hearing model is a holistic model of the hearingsystem of a recipient of an auditory prosthesis, and the virtual hearingmodel accounts for, in a bilateral manner, operation of each of theouter ears, middle ears, and inner ear systems of the recipient, as wellas the hearing cognition in auditory cortex and brain of the recipient,and how the auditory prosthesis operates and aids the perception of therecipient. At 694, the virtual hearing model is used to determineselected sound processing settings for use by the auditory prosthesis.At 695, the selected sound processing settings are instantiated (e.g.,sent to, installed, and activated) at the auditory prosthesis.

It is to be appreciated that the above described embodiments are notmutually exclusive and that the various embodiments can be combined invarious manners and arrangements.

The invention described and claimed herein is not to be limited in scopeby the specific preferred embodiments herein disclosed, since theseembodiments are intended as illustrations, and not limitations, ofseveral aspects of the invention. Any equivalent embodiments areintended to be within the scope of this invention. Indeed, variousmodifications of the invention in addition to those shown and describedherein will become apparent to those skilled in the art from theforegoing description. Such modifications are also intended to fallwithin the scope of the appended claims.

1-20. (canceled)
 21. A method, comprising: at a computing system:receiving a request for sound processing settings for a hearing deviceconfigured to be worn by a recipient, wherein the request includeslistening situation data representing an expected listening situationfor the hearing device; determining, based on a model of an auditorysystem of the recipient, selected sound processing settings for use bythe hearing device in the expected listening situation; and sending theselected sound processing settings to the hearing device.
 22. The methodof claim 21, wherein the model of the auditory system of the recipientaccounts for, in a bilateral manner, operation of each of the outerears, middle ears, and inner ear systems of the recipient, as well asthe hearing cognition in the auditory cortex and the brain of therecipient, and how the hearing device operates and aids the perceptionof the recipient.
 23. The method of claim 21, wherein determining theselected sound processing settings for use by the hearing device in theexpected listening situation comprises: iteratively performingsimulations for the model in at least one listening situation matchingthe expected listening situation; in each iteration, adjusting asimulated operation of the hearing device in the model to account fordifferent sound processing settings of the hearing device; anddetermining, as the selected sound processing settings for use by thehearing device, a set of sound processing settings estimated to providea selected hearing perception for the recipient in the at least onelistening situation matching the expected listening situation.
 24. Themethod of claim 23, further comprising: prior to receiving the requestfrom the hearing device, iteratively performing simulations for themodel in a plurality of different listening situations, wherein at leastone of the plurality of different listening situations comprises the atleast one listening situation matching the expected listening situation;following receipt of the request from the hearing device, identifyingthe at least one listening situation matching the expected listeningsituation; and determining, as the selected sound processing settingsfor use by the hearing device, a set of sound processing settingspreviously estimated to provide a selected hearing perception for therecipient in the at least one listening situation matching the expectedlistening situation.
 25. The method of claim 23, further comprising:following receipt of the request from the hearing device, generating,based on the listening situation data, the at least one listeningsituation matching the expected listening situation; iterativelyperforming simulations for the model of the auditory system of therecipient in the at least one listening situation matching the expectedlistening situation; in each iteration, adjusting the simulatedoperation of the hearing device in the model to account for differentsound processing settings of the hearing device; and determining, as theselected sound processing settings for use by the hearing device, a setof sound processing settings estimated to provide a selected hearingperception for the recipient in the at least one listening situationmatching the expected listening situation.
 26. The method of claim 23,wherein adjusting the simulated operation of the hearing device in themodel in each iteration comprises: adjusting the simulated operation ofthe hearing device in the model using a machine-learning algorithm. 27.The method of claim 23, wherein adjusting the simulated operation of thehearing device in the model in each iteration comprises: adjusting thesimulated operation of the hearing device in the model using anartificial intelligence algorithm.
 28. The method of claim 21, furthercomprising: generating the model based on recipient-specificpsychoacoustics data.
 29. The method of claim 21, further comprising:generating the model based on psychoacoustics data gathered from one ormore selected populations of hearing device recipients.
 30. A method,comprising: generating, based on recipient-specific psychoacousticsdata, a hearing model, wherein the hearing model is a model of anauditory system of a recipient of a hearing device, and wherein thehearing model accounts for, in a bilateral manner, operation of each ofthe outer ears, middle ears, and inner ear systems of the recipient, aswell as-a hearing cognition in an auditory cortex and brain of therecipient, and how the hearing device operates and aids a perception ofthe recipient; using the hearing model to determine selected soundprocessing settings for use by the auditory prosthesis; andinstantiating the selected sound processing settings at the hearingdevice.
 31. The method of claim 30, wherein using the hearing model todetermine selected sound processing settings for use by the hearingdevice comprises: receiving, from the hearing device, a request forsound processing settings, wherein the request includes listeningsituation data representing an expected listening situation for thehearing device; iteratively performing simulations for the hearing modelin at least one listening situation matching the expected listeningsituation; in each iteration, adjusting a simulated operation of thehearing device in the hearing model to account for different soundprocessing settings of the hearing device; and determining, as theselected sound processing settings for use by the hearing device, a setof sound processing settings estimated to provide a selected hearingperception for the recipient in the at least one listening situationmatching the expected listening situation.
 32. The method of claim 31,further comprising: prior to receiving the request from the hearingdevice, iteratively performing simulations for the hearing model in aplurality of different listening situations, wherein at least one of theplurality of different listening situations comprises the at least onelistening situation matching the expected listening situation; followingreceipt of the request from the hearing device, identifying the at leastone listening situation matching the expected listening situation; anddetermining, as the selected sound processing settings for use by thehearing device, a set of sound processing settings previously estimatedto provide a selected hearing perception for the recipient in the atleast one listening situation matching the expected listening situation.33. The method of claim 31, further comprising: following receipt of therequest from the hearing device, generating, based on the listeningsituation data, the at least one listening situation matching theexpected listening situation; iteratively performing simulations for thehearing model in the at least one listening situation matching theexpected listening situation; in each iteration, adjusting the simulatedoperation of the hearing device in the hearing model to account fordifferent sound processing settings of the hearing device; anddetermining, as the selected sound processing settings for use by thehearing device, a set of sound processing settings estimated to providea selected hearing perception for the recipient in the at least onelistening situation matching the expected listening situation.
 34. Themethod of claim 30, further comprising: generating the hearing modelbased on psychoacoustics data gathered from one or more selectedpopulations of hearing device recipients.
 35. The method of claim 30,wherein instantiating the selected sound processing setting at thehearing device includes: sending the selected sound processing settingto the hearing device.
 36. One or more non-transitory computer readablestorage media comprising instructions that, when executed by aprocessor, cause the processor to: obtain a recipient-specific model ofa perceptual system of a recipient of an implantable medical device;obtain situation data representing expected future use situations forthe implantable medical device; and determine settings for use by theimplantable medical device in the expected future use situations basedon the situation data and the recipient-specific model.
 37. The one ormore non-transitory computer readable storage of claim 36, furthercomprising instructions operable to: provide the settings to theimplantable medical device.
 38. The one or more non-transitory computerreadable storage of claim 36, wherein the implantable medical device isa hearing device, and wherein the situation data comprises listeningsituation data representing one or more expected listening situations.39. The one or more non-transitory computer readable storage of claim36, wherein the implantable medical device is an hearing device, andwherein the recipient-specific model is a model of an auditory system ofthe recipient accounting for, in a bilateral manner, operation of eachof the outer ears, middle ears, and inner ear systems of the recipient,as well as the hearing cognition in the auditory cortex and the brain ofthe recipient, and how the hearing device operates and aids theperception of the recipient.
 40. The one or more non-transitory computerreadable storage of claim 36, wherein the instructions operable todetermine the settings for use by the implantable medical device in theexpected future use situations comprise instructions operable to:iteratively perform simulations for the recipient-specific model in atleast one situation matching at least one expected future use situation;in each iteration, adjust a simulated operation of the implantablemedical device in the recipient-specific model to account for differentsettings of the implantable medical device; and determine, as thesettings for use by the implantable medical device, a set of settingsestimated to provide a selected perception for the recipient in the atleast one situation matching the at least one expected future usesituation.
 41. The one or more non-transitory computer readable storageof claim 41, wherein the instructions operable to adjust the simulatedoperation of the implantable medical device in the recipient-specificmodel in each iteration comprise instructions operable to: adjust thesimulated operation of the implantable medical device in therecipient-specific model using a machine-learning algorithm.
 42. The oneor more non-transitory computer readable storage of claim 41, whereinthe instructions operable to adjust the simulated operation of theimplantable medical device in the recipient-specific model in eachiteration comprise instructions operable to: adjust the simulatedoperation of the implantable medical device in the recipient-specificmodel using an artificial intelligence algorithm.